In this lab, we walk through scenarios to illustrate the use of process data and machine condition data and a layered approach to maintenance via usage-based, condition-based and predictive maintenance.

By the end of the course, you will be able to:

Discuss PI System’s role in maintenance and reliability – and explain the layered approach to usage-based, condition-based, predictive (simple and advanced) maintenance.

Use the techniques learnt in the lab to use the PI System tools to implement usage-based, condition-based, predictive (simple and advanced) maintenance.

Data sources include traditional plant instrumentation such as PLCs and SCADA, the newer IoT devices, and from machine condition monitoring such as vibration, oil analysis etc.

In this lab, we will also discuss predictive maintenance use cases that require advanced analytics, including machine learning, such as APR (advanced pattern recognition), anomaly detection, and others.

Who should attend? Experienced PI user

Summary

This lab’s objective is to walk-through the use of equipment and process data for a layered approach to uptime and reliability via usage based, condition-based and predictive – simple and advanced (machine learning) - maintenance.

Exercise 3b: CM, PM and PdM - Using engine failure history to support the decision criteria and quantify the benefits for moving from corrective maintenance (CM) to preventive maintenance (PM) to predictive maintenance (PdM)

For details of PdM multivariate model development, i.e. early fault detection via machine learning for predicting failure within a time window, see...more.

In this exercise, we assess the condition of an equipment by calculating metrics that can serve as leading indicators of equipment failure or loss of efficiency – for example, bearing temperature to evaluate the pump bearing condition.

We track the alerts for the bearing temperature and then discuss the use of PI Notification to send an email or use the web service delivery channel to notify a system (i.e. triggering a work order in a work management system such as SAP or IBM Maximo) for follow-up action.

The bearing temperature events are viewed in a watchlist in PI Vision – see screens below.

Exercise 3a: Predictive Maintenance (PdM) - bearing vibration trend

For certain classes of process equipment, their condition can be evaluated by monitoring some key metric, such as efficiency for a compressor, fouling for a heat-exchanger, bearing vibration on a pump, etc. Often, these metrics show a pattern with time – and, linear, piece-wise linear or non-linear trend can be extrapolated to estimate remaining-useful-life.

The screen below shows increasing vibration over time (100+ days). The trend can be extrapolated to estimate when it will reach a defined threshold and schedule maintenance.

In a deployment with about 100 similar engines, sensor data such as rpm, burner fuel/air ratio, pressure at fan inlet, and twenty other measurements plus settings for each engine – for a total of about 2000 tags – are available. On average, an engine fails after 206 cycles, but it varies widely - from about 130 to 360 cycles – each cycle is about one hour.

With a given failure history for the engines and known costs for PMs vs. repairs, we calculate the benefits in moving from CM to PM to PdM.

As part of the lab, we discuss:

 Can you quantify the $ spent on maintenance with the break-fix strategy (corrective maintenance)? A sister company with similar operations, failure history and repair/PM costs uses the median failure rate of 199 cycles for PMs. Should you adopt this? Can you do better? If so, after how many cycles will you do the PMs? Can you quantify the benefits in moving from corrective to run-hours based PMs?

 If engine operations data can be used for early detection of failure – say, within 20 cycles of a failure with 100% certainty – if and how much will you save by using PdM vs the PM approach

For details of PdM model development, i.e. early fault detection via machine learning for predicting failure within a time window, see...more.

In this Exercise, you apply the appropriate condition assessment rules and corresponding weighting factors to process/equipment measurements to calculate an overall asset health score.

It uses AF Analytics to convert a “Raw Value” (sensor data) to a normalized i.e. a “Case Value”. And then, by applying a Weight%, it is transformed to a Score.

Each measurement gets a normalized weighted score (0 to 10) by applying a condition assessment rule. And, then the normalized scores are rolled up to arrive at a composite asset health score. The Weight% applied to each attribute depends on its contribution to the overall asset health.

The composite asset health score ranges from 0 to 10 (0=Good, 10=Bad)

A Transformer asset health score example is used with the following measurements:

LTC (Load Tap Changer) counter operations

LTC through neutral count

DGA (dissolved gas analysis) detectable acetylene

DGA high gas rate of change

Low dielectric

High water

Low nitrogen pressure

An example Transformer template is shown below:

And, as you configure Transformers using these templates, the composite health score is periodically calculated by PI System Asset Analytics.

The composite health score for transformer TR01 is 2 i.e. asset is in good health (0=Good, 10=Bad).

Recap

In this lab, we covered scenarios to illustrate the use of process data and machine condition data and a layered approach to maintenance via usage-based, condition-based and predictive maintenance.

Quiz

The quiz is not intended to test “recall” – but is more about synthesizing the concepts from the lab and the PI System's role in the maintenance decision process.

1. To implement run-hours or similar counter-based logic, it is a prerequisite to have such counter measurements directly from the SCADA or PLC or other such instrumentation for the equipment._ True _ False

4. In Exercise 3b, the median failure of 199 cycles is the recommended threshold for PMs._ True _ False

5.Asset health score (Exercise 4) is more useful for comparison among peer-group assets than for comparing across different asset classes (motors, transformers, heat-exchangers…).

_ True _ False

6. In a mature maintenance setting, you will typically have a mix of all the different layers – reactive, usage-based, condition-based, and predictive deployed._ True _ False

7. For usage-based, it is recommended to do lifetime total calculations instead of doing meter reset in PI after every PM._ True _ False

8. When adopting predictive maintenance, you must start with advanced machine learning models for quick wins.

_ True _ False

9. Exercise 2 shows PI Notification via email for high bearing temperature alert. Event frames and PI Notification (web service delivery channel) can also be utilized as a basis for triggering a service request in work management systems such as SAP PM, IBM Maximo, and others._ True _ False

10. To incorporate the layered approach to maintenance, you can simultaneously deploy one or more of the appropriate techniques (reactive, usage-based, condition-based, and predictive) for an equipment and its components.

_ True _ False

11. To adopt the linear extrapolation example illustrated in Exercise 3a, you must fit the entire vibration history to ensure that all available data is used._ True _ False

13. For usage-based, it is a good practice to use year-to-date calculations as illustrated by:

_ True _ False

14. AF calculations can include machine condition data such as oil analysis, vibration data etc. that may not be natively in PI tags but are available externally and referenced via table look-ups or other data reference methods._ True _ False

15. Integration with work management systems such as SAP-PM, IBM Maximo etc. is a prerequisite to adopting the layered approach to maintenance shown in this lab._ True _ False